Monday March 28, 2016

Eradicating worldwide poverty by 2030 is the top goal on the United Nations’ sustainable development agenda, published late last year. But a lack of data has frustrated efforts to measure progress toward the goal. Most of those living in extreme poverty are in sub-Saharan Africa and Southern Asia, where accurate poverty data is scarce. A small team at Stanford University is changing that, one satellite image at a time.

Machine learning expert Stefano Ermon partnered with food security specialists David Lobell and Marshall Burke, plus a couple Stanford engineering students, to turn Google Earth images into statistical poverty models. "We want to end extreme poverty, but we need a way to be able to measure whether we’re making progress or not," said Ermon, an assistant professor of computer science at Stanford.

Using NVIDIA GPUs, the team trained a neural network to accurately predict poverty levels in sub-Saharan Africa from image features like roads, farmlands and homes. This work has placed Stanford among five finalists for NVIDIA’s 2016 Global Impact Award. Each year, we award a $150,000 grant to researchers using NVIDIA technology for groundbreaking work that addresses social, humanitarian and environmental problems.